Document Type

Journal Article

Department/Unit

Department of Mathematics

Language

English

Abstract

Varying-coefficient models are widely used to model nonparametric interaction and recently adopted to analyze longitudinal data measured repeatedly over time. We focus on high-dimensional longitudinal observations in this article. A novel two-step sparse boosting approach is proposed to carry out the variable selection and the model-based prediction. As a new machine learning tool, boosting provides seamless integration of model estimation and variable selection for complicated regression functions. Specifically, in the first step the sparse boosting technique assuming independence is applied to facilitate an initial estimate of the correlation structure while in the second step the estimated correlation structure is incorporated in the loss function of the sparse boosting algorithm. Extensive numerical examples illustrate the advantage of the two-step sparse boosting method. An application of yeast cell cycle gene expression data is further provided to demonstrate the proposed methodology.

Keywords

Sparse boosting, Variable selection, Longitudinal data, Varying-coefficient model, Minimum description length

Publication Date

3-2019

Source Publication Title

Computational Statistics and Data Analysis

Volume

131

Start Page

222

End Page

234

Publisher

Elsevier

DOI

10.1016/j.csda.2018.10.002

Link to Publisher's Edition

https://doi.org/10.1016/j.csda.2018.10.002

ISSN (print)

01679473

ISSN (electronic)

18727352

Available for download on Thursday, April 01, 2021

Included in

Mathematics Commons

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